Data Summarization with Social Contexts

Abstract

While social data is being widely used in various applications such as sentiment analysis and trend prediction, its sheer size also presents great challenges for storing, sharing and processing such data. These challenges can be addressed by data summarization which transforms the original dataset into a smaller, yet still useful, subset. Existing methods find such subsets with objective functions based on data properties such as representativeness or informativeness but do not exploit social contexts, which are distinct characteristics of social data. Further, till date very little work has focused on topic preserving data summarization, despite the abundant work on topic modeling. This is a challenging task for two reasons. First, since topic model is based on latent variables, existing methods are not well-suited to capture latent topics. Second, it is difficult to find such social contexts that provide valuable information for building effective topic-preserving summarization model. To tackle these challenges, in this paper, we focus on exploiting social contexts to summarize social data while preserving topics in the original dataset. We take Twitter data as a case study. Through analyzing Twitter data, we discover two social contexts which are important for topic generation and dissemination, namely (i) CrowdExp topic score that captures the influence of both the crowd and the expert users in Twitter and (ii) Retweet topic score that captures the influence of Twitter users' actions. We conduct extensive experiments on two real-world Twitter datasets using two applications. The experimental results show that, by leveraging social contexts, our proposed solution can enhance topic-preserving data summarization and improve application performance by up to 18%.

Efficient Representative Subset Selection over Sliding Windows

“…RSS selects a set of representative elements from the ground set according to a utility function with some budget constraint. In this paper, we target the class of nonnegative monotone submodular utility functions adopted in a wide range of RSS problems [2,6,10,24,32,37,40].…”

“…Social stream summarization aims to retain a small portion of representative elements from a user-generated stream. One common approach is topic-preserving summarization [32,40] that selects a subset of posts that best preserve latent topics in the stream. We focus on topic-preserving summarization in the sliding window model to capture the evolving nature of social streams, i.e., topics under discussion change over time [32].…”

“…An imperative task is to extract useful information from massive datasets. A compelling approach is representative subset selection [2,10,23,24,25,26,35,36,37,40] (RSS): extracting a concise subset of representative elements from the source dataset. RSS is often formulated as selecting a subset of elements to maximize a utility function that quantifies the representativeness subject to some constraints.…”

“…RSS is often formulated as selecting a subset of elements to maximize a utility function that quantifies the representativeness subject to some constraints. The utility functions are often chosen to be submodular to capture the "diminishing returns" property of representativeness [2,7,10,32,38,40], i.e., adding more elements decreases the marginal representativeness. A number of constraints are used to restrict the selected subset in various ways.…”

Scalable Approximation Algorithm for Graph Summarization

“…This technique does not use any structural information of the graph. In [8] social contexts and characteristics are used to summarize social networks. Summarization of edge-weighted graphs is studied in [9].…”